Adaptive Image Denoising with Block-Rotation-Based SVD Filtering and Edge Detection

Abstract

ABSTRACT Image denoising is essential in image processing. However, existing denoising methods are not optimized for the images with distinct straight edges and lines that traverse in different directions (such as images used in autonomous driving). This paper proposes an image denoising method that uses singular value decomposition (SVD) and block-rotation-based operations and has two features: the non-fixed size of block division and the rotation operations. In the method, we first propose an image division approach, which is used to divide an image into sub-blocks of different sizes, to ensure that the line(s) or edge(s) in each sub-block have roughly one main direction. Second, we decompose an image into sub-blocks according to the image division approach. Third, we rotate each sub-block to ensure that the main direction of the edge(s) is horizontal or vertical. Fourth, we perform SVD on each sub-block, and we use the low-rank approximation of SVD to obtain each denoised sub-block. Finally, we rotate the approximation of each sub-block back to the original direction of the corresponding noisy sub-block, and then we reconstruct the denoised image using these denoised sub-blocks. Experiments show the effectiveness of this method compared with the SVD-based methods.